Software applications and systems have become indispensable tools for helping consumers, i.e., users, perform a wide variety of tasks in their daily professional and personal lives. Currently, numerous types of desktop, web-based, and cloud-based software systems are available to help users perform a plethora of tasks ranging from basic computing system operations and word processing, to financial management, small business management, tax preparation, health tracking and healthcare management, as well as other personal and business endeavors, operations, and functions far too numerous to individually delineate here.
One major, if not determinative, factor in the utility, and ultimate commercial success, of a given software system of any type is the ability to implement and provide a customer support system through which a given user can obtain assistance and, in particular, get answers to questions that arise during the installation and operation of the software system. However, providing potentially millions of software system users specialized advice and answers to their specific questions is a huge undertaking that can easily, and rapidly, become economically infeasible.
To address this problem, many providers of software systems implement or sponsor one or more question and answer based customer support systems. Typically, a question and answer based customer support system includes a hosted forum through which an asking user can direct their specific questions, typically in a text format, to a support community that often includes other users and/or professional agent support personnel.
In many cases, once an asking user's specific question is answered by members of the support community through the question and answer based customer support system, the asking user's specific question, and the answer to the specific question provided by the support community, is categorized and added to a customer support question and answer database associated with the question and answer based customer support system. In this way, subsequent searching users, i.e., a user accessing previously generated question and answer pairs, of the software system can access the asking users' specific questions or topics, and find the answer to the asking users' questions, via a search of the customer support question and answer database. As a result, a dynamic customer support question and answer database of categorized/indexed asking users questions and answers is made available to searching users of the software system through the question and answer based customer support system.
The development of customer support question and answer databases has numerous advantages including a self-help element whereby a searching user can find an answer to their particular question by simply searching the customer support question and answer database for topics, questions, and answers related to their issue previously submitted by asking users. Consequently, using a question and answer based customer support system including a customer support question and answer database, potentially millions of user questions can be answered in an efficient and effective manner, and with minimal duplicative effort.
Using currently available question and answer based customer support systems, once an asking user's question is answered, the asking user is provided the opportunity to rate the answer with respect to how helpful the answer was to the asking user. In addition, searching users in the user community are provided the opportunity to rate accessed question and answer pair content based on how helpful the answer was to them. In this way, feedback is provided with respect to a given question and answer pair, and answers with low satisfaction ratings, i.e., poorly rated answers, can eventually be identified by this feedback. In addition, this feedback data is also often used to determine/rank which question and answer pair, or pairs, to provide a searching user in response to question content submitted by the searching user.
Using traditional customer support question and answer databases, when a searching user submits a question, e.g., submits question data, to the customer support question and answer database, the customer support question and answer database is searched to determine if the question currently being asked has been answered before. Typically, if a determination is made that the question currently being asked, or a sufficiently similar question, has been answered before, the searching user is then provided one or more answers previously provided to the previously submitted questions determined to be the same as, or sufficiently similar to, the question currently being asked. Typically the searching user is then provided results data representing one or more previously asked question and answer pairs.
In addition, it often happens that there are multiple previously answered questions substantially identical to, or sufficiently similar to, the question currently being asked. As a result, there are often multiple previously generated answers related to the question currently being asked. In these cases, a determination must be made as to which previously answered question and answer pair, or pairs, are most likely to provide the current searching user with an answer to the current question being asked that will result in the highest probability of the searching user being satisfied with the answer provided, i.e., a previously answered question and answer pair must be chosen that is most likely to result in user satisfaction with the answer provided.
Using traditional customer support question and answer databases, the determination as to which previously answered question and answer pair, or pairs, are most likely to result in the searching user being satisfied with the answer provided is made largely, if not entirely, based on the feedback data, or ranking data, associated with the previously answered question and answer pair data provided by the original asking user and/or subsequent searching users as discussed above. As a result, using current question and answer based customer support systems, and their associated customer support question and answer databases, poorly rated, or low quality/value question and answer pair data is only removed reactively, after it has potentially been viewed by multiple users, and often a large number of searching users.
In addition, the determination as to which previously answered question and answer pair, or pairs, are most likely to result in the searching user being satisfied with the answer can only be made reactively after feedback data, or ranking data, associated with the previously answered question and answer pair data is provided by the original asking user and/or subsequent searching users. This is particularly problematic because until feedback data, or ranking data, regarding previously answered question and answer pair data is received from a significant number of users, the determination as to which previously answered question and answer pair, or pairs, are most likely to result in the searching user being satisfied with the answer can only be made on, at best, likely skewed and inaccurate data.
Consequently, by the time poorly rated question and answer pair data is identified and ranked by receipt of a threshold number of low satisfaction ratings, such as a “down vote,” or a threshold number of high satisfaction ratings, such as an “up vote,” not only is the initial asking user potentially dissatisfied with the answer content, and often with the software system itself, but searching users, and often numerous additional searching users, are also potentially dissatisfied with the previously answered question and answer pair data provided to them in response to their currently submitted question data. This then leads to the searching users becoming dissatisfied with the support provided, and the software system itself. In addition, these current methods for ranking question and answer pair content and identifying low quality/value question and answer pair data are based on the assumption that the asking and searching users will not only provide feedback, but that they will provide feedback that is objective and logical, e.g., not based on emotion or frustration; often, this is simply not the case.
The above situation presents several challenges to the providers of question and answer based customer support systems, and their associated customer support question and answer databases. These challenges are partially significant given that a customer support question and answer database is usually a critical, if not the most important, feature of a question and answer based customer support system. This is because there is, by definition, a finite number of support resources, such as, for example, support personnel, either volunteers or professionals, and, therefore, there is a limit to the amount of support resources, such as support person-hours, available at any time to answer user questions. Consequently, it is important to utilize support resources, such as a support community, efficiently to answer not only just new questions, and thereby avoid redundant efforts, but to answer questions that are likely to result in satisfied users first, as opposed to questions that are unlikely to satisfy either the asking user or subsequent searching users.
One important consideration in developing an effective and efficient question and answer based customer support system, and an associated customer support question and answer database, is the need to provide a customer support question and answer database having as much, and as varied, question and answer pair data as possible, as soon as possible. That is to say, the providers of question and answer based customer support systems, and their associated customer support question and answer databases, need to develop question and answer pair data representing as large a number of different question and answer pairs as they can to maximize the capability to answer as many potential questions as possible without resorting to utilizing the limited number of support personnel or other support resources. To achieve this goal, the support resources, such as a support community, must be used to first answer numerous questions that are new. Consequently, in order to develop, and/or dynamically adapt, the customer support question and answer database, the support resources, such as volunteer and professional agent support personnel of the support community, must initially be heavily be utilized.
However, it is at least equally important to ensure the question and answer pair data is high quality/high value question and answer pair data, i.e., that the resulting question and answer pair content is likely to result in both asking and searching user satisfaction with the answer/assistance offered through the question and answer pair data. In short, it is highly desirable to ensure that the support resources, such as volunteer and professional agent support personnel of the support community, are utilized to provide is high quality/high value question and answer pair data associated with as many varied question types in as short a time as possible.
As noted above, to most efficiently utilize support resources, such as volunteer and professional agent support personnel of a support community, it is desirable to focus those support resources on submitted questions that are new question types to avoid redundant effort. Most traditional customer support question and answer databases make some effort to this end. However, it is equally important to ensure support resources, such as volunteer and professional agent support personnel of a support community, are utilized to answer questions that are likely to not only provide the asking user with an answer that will result in the asking user being satisfied with the answer content provided, but that are also likely to be useful to other searching users, and result in these searching users being satisfied with the answer content provided. That is to say, it is equally important to ensure the support resources, such as volunteer and professional agent support personnel of a support community, are focused on generating high quality/high value question and answer pair content. In this way, the use of the support resources, such as volunteer and professional agent support personnel of a support community, will yield more positive results and a customer support question and answer database will be developed, and/or dynamically adapted, to provide higher quality answer content predicted to provide a greater number of users with answer content meeting their needs.
Despite this long standing need, traditional question and answer based customer support systems typically do not address the issue discussed above. This is largely because, as noted above, using traditional question and answer based customer support systems, analysis of question and answer data is largely preformed reactively only after the answer data has been generated, and after the support resources, such as volunteer and professional agent support personnel of a support community, have been devoted to answering the question. Consequently, using traditional question and answer based customer support systems, precious support resources, such as volunteer and professional agent support personnel time, are often devoted to low quality/low value questions, while high quality/high value questions wait to be answered, and therefore are delayed before being added to the customer support question and answer database where other searching users can access the question and answer pair content.
In addition, to make matters worse, it is often the case that much more precious support resources, such as volunteer and professional agent support personnel time, are wasted trying to answer a low quality/low value question than would be required to answer a high quality/high value question. That is to say, ironically, as a general rule, it takes longer and more effort to produce low quality/low value question and answer pair data than to produce high quality/high value question and answer pair data. Worse yet, the longer time devoted to trying to answer the low quality/low value questions is often completely wasted because, by definition, neither the asking or searching users are likely to be satisfied with the answer data provided. Indeed, by providing answer data unlikely to result in satisfied users, it is arguable the provider of the software system is devoting a disproportionate amount of precious support resources to an endeavor that, at best, will provide no positive result, and often results in poor user ratings and dissatisfaction with the software system itself.
Clearly, the situation described above represents a significant issue and a long standing problem for question and answer based customer support systems and software system providers. This is because user satisfaction with the question and answer based customer support systems is not only critical to the effectiveness of the question and answer based customer support systems, but also to the satisfaction and reputation of the software system and the software system provider. As a result of the situation described above, currently, both users and providers of software systems, and question and answer based customer support systems of all types, are denied the full potential of the question and answer based customer support systems. Consequently, the technical fields of information dissemination, customer support, feedback utilization and integration, software implementation and operation, and user experience are detrimentally affected.
What is needed is a method and system for reliably, efficiently, and proactively predicting user satisfaction with a potential answer to a user's question and then routing and prioritizing the user's question to support resources, such as volunteer and/or professional agent support personnel of a support community, such that those questions predicted to result in high user satisfaction with any answer data generated are provided to the support resources, such as the proper support personnel, in a priority manner, while those questions predicted to result in low user satisfaction with any answer data generated are subjected to one or more corrective actions before being submitted to any support resources, such as support personnel, or are at least given a lower priority for submission to the support resources, such as proper support personnel. In this way, the satisfaction of asking and/or searching users with question and answer pair data to be potentially provided through the question and answer based customer support system can be predicted to ensure support resources, such as the time and energy of support personnel, are utilized most efficiently and the resulting question and answer pair data is of the highest quality/value.